Analysis of right-turn vehicular driving paths at uncontrolled T-intersections

Abstract

Turning vehicle maneuvers highly influence vehicular safety at uncontrolled T-intersections due to their complexity and the inattentive behaviour of vehicle drivers. It was observed that the drivers while maneuvering from a minor road to a major road (viz., left-side driving in an Indian context) were distracted from conventional driving paths, which may considerably increase the risk of a crash. The present study examined the impact of various factors on driving path distractions. To fulfil the objective, a Binary Logit Model (BLM) and machine learning techniques, viz. Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forest (RF) models, were developed by considering driving path distractions as a dependent variable and the remaining set variables as independent variables. The model results revealed that driving path distractions are highly sensitive to the turning vehicle type, maneuvering speed of turning vehicles, running speed of through vehicles on a major road, through traffic volume, vehicle gap, waiting time, and right-turning traffic volume. Further, the study concluded that maneuvering speed, waiting time, and adequate available vehicular gaps significantly impact the behaviour of vehicular drivers when changing lanes. Field engineers could use the results of this study to start taking control measures at uncontrolled T-intersections.

Keywords:

• driving paths
• driver behaviour
• binary logit model
• machine learning Techniques
• uncontrolled T-intersections

Disclosure statement

No potential conflict of interest was reported by the author(s).

Funding

The author(s) reported there is no funding associated with the work featured in this article.